Production-grade reference documentation for in-process spatial SQL: run geospatial analytics directly inside DuckDB with vectorized, columnar execution — no GIS server, no row-by-row round trips.
These guides target data engineers, GIS analysts, and Python developers who need deterministic performance at scale. You'll find the execution model behind DuckDB Spatial, the query patterns that keep spatial joins and aggregations vectorized, and the integration paths that move geometries between SQL and Python without serialization overhead.
Every page is grounded in real configuration: memory limits and spill thresholds, CRS handling, GeoParquet and GeoJSON ingestion, execution-plan validation, and a full migration track for teams moving off PostGIS or GeoPandas — function-by-function translation, index porting, and the benchmarks that mark the performance crossover.
Four tracks, from engine internals to query craft to Python pipelines and PostGIS/GeoPandas migration.
The execution model: vectorized columnar processing, GeoParquet/GeoJSON ingestion, CRS handling, spatial indexing internals, and in-memory vs disk tradeoffs.
Move workloads off PostGIS and GeoPandas: function and index translation, replacing spatial joins, CRS workflow porting, and the performance crossover points that justify the switch.
Engineering-grade query patterns: spatial joins and proximity filters, vectorized aggregations, window functions, and regression-safe diagnostics.
Zero-copy Python integration: async execution, batch pipelines, Shapely interop, and efficient DuckDB → GeoPandas synchronization.
Hand-picked deep dives — the fastest way into the patterns engineers reach for most.